What Will You Do With What You Learn From IoT?

When the Internet of Things (IoT) started to emerge as a popular topic, I had to stop and ask myself if I was once again going to provide commentary on this emerging field. I enjoy exploring new technology shifts and illustrating how they can benefit various industries and businesses. It’s what I’ve done for the past 20 years through Java, XML, Web Services, SOA, Cloud and DevOps. However, every time I started writing on IoT I seemed to run into the same conundrum; am I commenting on this to jump on the hype bandwagon or because I see a need to represent the pragmatics of implementing and adopting this technology.

There is no question that more sensory inputs can lead to greater understanding. The ability to monitor a thing with fine granularity facilitates greater learning. It’s why we have research studies that have extended over multiple decades. It is the basis of the scientific method enabling systematic observation and measurement of phenomena. We probably have only begun to explore the boundaries of what we might learn through these observations and this growing sensor network. In most cases today IoT focuses on observation of a single “thing”, but ultimately we will learn of patterns that occur due to one “thing” impacting another “thing”. These chains of events have the capability to drive innovation that till now has only been described in science fiction. It’s Schroedinger’s experiment applied on an infinite scale.

There’s some pragmatic applications of this technology that can be applied to a whole host of industries: healthcare, manufacturing, energy, civil planning, physical security, etc. In fact, many of these applications have been in production for decades. The military has been using sensors in battlefield scenarios since 2003. The energy sector has been using sensors to monitor oil pipelines since 2005. Most of these used proprietary protocols operating over low-bandwidth transmission mediums, but they effectively deliver the value that we expect from IoT today.

So, part one of my conundrum is the question, “so what’s new?” Systems that provide value typically tend to improve over time. Today, the hardware is less expensive, it’s easier and less expensive to develop handlers for the data, we are leveraging higher bandwidth wireless mediums that allows us to use TCP/IP based (standardized) protocols. But, isn’t this just really an improvement on the existing implementations? Is it worthy of the hype? Perhaps it’s the fact that due these factors we can see a “social” effect. That is, just like Facebook an LinkedIn, as more sensors join the network the value of the network increases exponentially. This is certainly worthy of the hype, but it seems the hype has been centered on the advancements in the technology underpinnings rather than the value of the network being created. Even where there has been focus on new applications for this technology, it’s certainly stuff that could have been produced over the past two decades.

Part two of my conundrum is even more difficult to tackle. What are we going to do with what we learn? Again, today, many of the applications of IoT typically deal with issues of what we’ll call “event processing” for lack of a more encompassing term. That is, an event is occurring and we can capture that event much closer to the event horizon now because we have a sensor providing real-time information. This event also generates data that may be discarded after the event has been processed and handled or it may be stored and cataloged to be analyzed at a future time. Assuming the latter we can start to identify patterns of behavior associated with this event, such as situational awareness–what is happening in the surrounding environment that causes the event to occur–and what are the factors indicative of the event itself–useful in root cause analysis. In the case where the pattern of behavior identified leads to behavioral changes we find ourselves in a precarious position.

Figure 1 below illustrates the issue

Since we’re living with layers of aging and deteriorating systems implementing learned behavioral changes becomes difficult as we have to either apply our learning to the existing systems or make new systems through modernization, refactoring or new development. As you can see by the illustration assimilating these changes becomes easier in a post-IT Transformation environment, but still will fall far short of the overall number of learned patterns which will exponentially grow as the overall sensor network grows.

This brings me back to my conundrum, which is if we’re not able to readily assimilate the new learned behaviors from IoT and the value of IoT is just that it’s gotten cheaper and easier to deliver event processing, is it worthy of the hype or is it on par with advancements in CPU processing? Please don’t get me wrong, I’m not saying that IoT does not have significant intrinsic value, because it does. Businesses that can apply this technology for the betterment of their products and services will see revenue benefits and operating cost reductions. Moreover, these sensors can be aggregated to drive that benefit across the supply-chain. For example, the airline that recognizes that it’s engine is at risk for a breakdown and pulls it out of service before it causes multiple service delays by leveraging the sensors provided by the engine manufacturer and aggregated into their sensor grid for the entire aircraft. In the biz, we call this a solution. If the airlines had asked, we could have delivered this solution sooner, but perhaps they didn’t particularly care for the associated price point. Hence, proving IoT has business value at a given price point. This is economics 101, but again is it hype worthy?

To summarize, I do believe if IoT advances to the point where it’s essentially an intelligent network that is generating and exposing new behavioral patterns that we cannot easily discern today because the data is absent, then IoT will have earned it’s hype-worthiness. Much the way we have learned much about human behavioral patterns through the use of social networking, “things” impact each other and the opportunity to observe that phenomena will pretty much reshape the entire planet. However, as an event processing engine that helps us park our car better?

2 thoughts on “What Will You Do With What You Learn From IoT?”

fwiw, I designed the architecture behind a large scale home automation IoT service.

Your points about integration and standardisation are very significant: IoT is currently mostly being implemented by domain experts (industrial control, medical, etc), who tend not to have a great understanding of the computing aspects. At the same time, many of the compute folk are not very familiar with the data errors and calibration that arise in IoT’s interaction with the real world. In fact, there’s a lot to bring to the architecture from SI and from ITSM.

You omit the actuator side of IoT. Is that deliberate? It introduces new security and timeliness issues.

The fundamental challenge in the near term is the commercial relationships in the supply chain: those who make ‘Things’ focus on building sales volume, driven by the ‘happy-path’ usecases; those who want to be in the services markets worry about the cost of support. I believe that addressing these aspects requires much more focus on testability: the production environment variation is enormous and emergent behaviour common.

The opinions expressed here are my personal opinions. Content published here is not read or approved in advance by Perficient and does not necessarily reflect the views and opinions of Perficient nor does it constitute any official communication of Perficient.